Gallagher et al. BMC Public Health (2016) 16:640 DOI 10.1186/s12889-016-3321-5
RESEARCH ARTICLE
Open Access
Factors associated with self-reported health: implications for screening level community-based health and environmental studies Jane E. Gallagher1, Adrien A. Wilkie2, Alissa Cordner3, Edward E. Hudgens1, Andrew J. Ghio1, Rebecca J. Birch4 and Timothy J. Wade1*
Abstract Background: Advocates for environmental justice, local, state, and national public health officials, exposure scientists, need broad-based health indices to identify vulnerable communities. Longitudinal studies show that perception of current health status predicts subsequent mortality, suggesting that self-reported health (SRH) may be useful in screening-level community assessments. This paper evaluates whether SRH is an appropriate surrogate indicator of health status by evaluating relationships between SRH and sociodemographic, lifestyle, and health care factors as well as serological indicators of nutrition, health risk, and environmental exposures. Methods: Data were combined from the 2003–2006 National Health and Nutrition Examination Surveys for 1372 nonsmoking 20–50 year olds. Ordinal and binary logistic regression was used to estimate odds ratios and 95 % confidence intervals of reporting poorer health based on measures of nutrition, health condition, environmental contaminants, and sociodemographic, health care, and lifestyle factors. Results: Poorer SRH was associated with several serological measures of nutrition, health condition, and biomarkers of toluene, cadmium, lead, and mercury exposure. Race/ethnicity, income, education, access to health care, food security, exercise, poor mental and physical health, prescription drug use, and multiple health outcome measures (e.g., diabetes, thyroid problems, asthma) were also associated with poorer SRH. Conclusion: Based on the many significant associations between SRH and serological assays of health risk, sociodemographic measures, health care access and utilization, and lifestyle factors, SRH appears to be a useful health indicator with potential relevance for screening level community-based health and environmental studies. Keywords: Self-reported health, Screening level health assessment, Clinical measures, Metal mixtures analyses, NHANES
* Correspondence: [email protected] 1 Environmental Public Health Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Mail Drop: 58C, Research Triangle Park, NC 27711, USA Full list of author information is available at the end of the article © 2016 Gallagher et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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Background Health outcomes are multi-determined and result from complex interactions of social, cultural, economic, psychosocial, environmental, and community factors. However, this wide range of factors are typically studied in a ‘siloed’ manner [1]. Effective public health policies can be generated only if a range of risks along the complex causal chain leading to health outcomes is assessed, defined, and studied comprehensively [2]. Self-reported health (SRH) is a qualitative singlequestion assessment of health [3]. SRH is commonly acquired in health surveys in the United States (e.g., MacArthur Field Study of Successful Aging, Hawaii Health Survey, San Luis Valley Diabetes Study, National Risk Survey, National Health and Nutrition Examination Survey [NHANES], and Robert Wood Johnson Foundation) [4–7] and internationally (e.g., Spanish National Disability Survey, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, National Population Health Survey, and Manitoba Longitudinal Study on Aging) [8–10]. SRH is also commonly used in psychological research, clinical settings, and in general population surveys [11]. Studies have shown that SRH is associated with lifestyle related diseases (e.g., diabetes and hypertension [12]), lifestyle habits (e.g., smoking status [13], regular physical exercise [14], obesity [15], and, most notably, subsequent mortality [4, 10]). The validity and value of SRH, with respect to mortality, is independent of clinical or physician assessments, and SRH surpasses these measures in predictive power [11]. Few studies link SRH with diagnostic clinical indicators of disease [12, 16, 17] and even fewer evaluate SRH in relation to blood or urinary based biomarkers of environmental exposure [17, 18]. Many diseases and health conditions are often not reported, thus county, state, and national surveys often have limited health outcome data [19]. Local, state, and national public health officials, exposure scientists, and environmental justice advocates would benefit from a screening level health status indicator, such as SRH, to identify potentially vulnerable communities and modifiable health risk factors. Such an indicator would also add value to studies where both environmental exposures and social determinants of health are simultaneously assessed [20]. This study investigates the utility of SRH as a general proxy for health status by investigating whether, and to what extent, SRH is associated with race/ethnicity and broad range of health-risk indicators (N = 57) thought to be important determinates of health. Data were extracted from NHANES and include race/ethnicity and health risk factors across six domains: sociodemographic,
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health care, health status (e.g., diseases/health conditions), lifestyle factors, serological clinical and nutritional indicators, and blood biomarkers of exposures for metals and volatile organic compounds.
Methods Physical, medical, laboratory, and respondent data from questionnaires and clinical analysis were extracted from publically available NHANES data from survey years 2003–2004 and 2005–2006. The data and more information about data collection are available online [21]. Data on SRH and a broad array of subjective and objective respondent characteristics, including sociodemographic indicators, health care, lifestyle factors, and diseases, were obtained from interviewer administrated computerassisted personal interviews conducted at the household interview and mobile examination center [22, 23]. While all NHANES participants complete a computer assisted personal interview, full serum analysis, including chemical exposure assessment, is conducted for only a randomly selected subset of NHANES participants. Study population
Of the full NHANES study sample of 5214 participants between the ages of twenty and fifty, the study population for this analysis is composed of 1372 twenty to fifty year old nonsmokers with complete data on SRH and serum biomarkers. Respondents (N = 1731) were omitted from the analysis if their serum cotinine concentration was greater than 10 ng/mL (N = 1648), or if serum cotinine was missing and they self-identified as a current smoker (N = 81), or if both were missing (N = 2). We restricted the analysis to current nonsmokers due to the adverse health impact associated with smoking. We did not want to overly influence (weaken or strengthen) any potential associations between SRH and the various factors by including smokers. We verified the suspected strong relationship between SRH and smoking in preliminary analyses (not shown) that found smokers, both self-identified current smokers and participants with cotinine measurements >10 ng/mL, were twice as likely to report poor/fair health as compared to nonsmokers. An additional 2111 respondents were excluded due to missing values for benzene and/or toluene (N = 1948) or due to missing data for SRH and pertinent demographic, body measurement, and clinical data (N = 163). If data were missing from less than 20 participants for other variables, those participants were excluded from analysis using that variable. If data were missing from more than 20 participants, a “missing” category was included for analysis of that variable. Sample sizes are provided in Tables 1 and 2. Some variables of interest were only analyzed for females (e.g., ferritin, transferrin receptor, transferrin saturation, iron, hemoglobin, and total iron
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Table 1 Sociodemographic characteristics of the study population (N = 1372 twenty to fifty year old nonsmokers) N
Weighted percenta (SE)
Male
547
46.4 (1.4)
Married
Female
825
53.6 (1.4)
Never married
315
20.1 (1.7)
Other
244
16.1 (1.3)
Sex
N
Weighted percenta (SE)
813
63.8 (2.0)
Marital Status
Race/Ethnicity Mexican American
350
10.8 (1.2)
Other hispanic
61
4.9 (0.9)
median of nonsmokers) f Benzene and toluene (1 if > median of nonsmokers)
constancy of the odds ratio across multiple categories. These two models provide complementary but different interpretations of the association between SRH and the health risk indicators. For a more rigorous comparison and discussion of binary and several ordered SRH analytic choices, see Manor et al. [29] and Barger [30]. Mexican Americans and non-Hispanic Blacks, when compared to non-Hispanic Whites, were more likely to report poorer SRH. These findings are consistent with Shetterly et al. [5] and Benjamins et al. [31]. Our analyses showed a strong association with poorer SRH with lower education and income levels. Lahelma et al. [32] explain the clear associations between health and education, occupational class, and family income. Adler and Ostrove [33] discussed how sociodemographic and environmental factors, individual psychological and behavioral factors, and biological predispositions and processes can impact health status. Associations were observed between poorer SRH and the number of days a respondent’s mental health was not good. This finding suggests that SRH incorporates a
mental health or psychosocial component that otherwise would go undetected in serological based clinical tests. Poorer SRH was associated with lower levels of Vitamin C, Vitamin D, and calcium. These findings are consistent with Radimer et al. [34], who showed that intake of multivitamin and multi-minerals dietary supplements by US adults was associated with very good/excellent self-reported health. Poorer SRH was associated with lower levels of HDL, higher levels CRP, triglycerides, serum glucose, glycohemoglobin, platelet count, elevated eosinophil, and lymphocyte number. Nine of eleven blood iron markers were associated with SRH. These health indicators are linked to cardiac health, diabetes risk, and other medical conditions. Of particular note in our study was the strong association between the RDW and poorer SRH. Several studies have reported strong associations between RDW and mortality, although the mechanism by which RDW influences health status is unknown [35–37]. Based on the strong associations observed between RDW and SRH and because RDW is
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routinely performed, RDW may serve as an important early indicator of adverse health status prior to disease onset. Biomarkers of chemical exposure
Blood levels of the three toxic heavy metals (cadmium, lead, mercury) and two VOCs (toluene and benzene) were evaluated in relation to SRH. All five chemicals have public health importance due to their environmental abundance and well-documented toxicity. SRH was associated with blood levels of cadmium, mercury, lead, and toluene but not benzene (perhaps because only 43 % of the respondents in this study were above the limit of detection for benzene). Examination of benzene in relation to health is of interest in light of studies showing ambient air levels of benzene and formaldehyde contribute nearly 60 % of the total cancer-related health impacts of air pollution in the United States [38]. People are exposed to mixtures of pollutants, through a variety of media, including air, water, and food. Thus, research is needed to better understand the cumulative risks posed to human health from the myriad of environmental contaminants that can occur simultaneously. Interactive effects of chemical within mixtures are complex and can result in alterations in the distribution, metabolism, absorption, and excretion of the chemicals [39]. Recently, Cobbina et al. [40] observed synergistic effects of metals mixtures which is consistent with our data where the odds of reporting poorer SRH were greater if the combined blood levels of mercury, lead, and cadmium were considered as opposed to each of the individual metals. In isolation, increasing levels of blood mercury were associated with a better SRH, an association that is likely confounded by income and fish consumption. For example, Mahaffey et al. [41] showed that blood mercury levels in women was related to higher income, consumption of fish, ethnicity, and residence (census region and coastal proximity). Higher blood lead and cadmium levels were associated with lower income levels [42]. Taken together, these studies underscore the need for further research into the relationships between health and cumulative exposures to chemicals, in the context of cultural, economic factors, especially for vulnerable populations and communities [43]. Our data suggest that SRH may be a useful screeninglevel indicator of health status for community-based health and environmental studies based on the number of associations of SRH of several sociodemographic, health care, health, lifestyle, serum-based nutritional, and serum-based environmental measurements. Examples of studies using screening level indices are those by Gallagher et al. [44] where health, sustainability, and environmental indices were derived for fifty major US cities. These diverse indices and associated indicators from
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which they were derived were associated with disparities related to race, education, and income. Messer et al. [45] applied a multidimensional neighborhood deprivation index (which considered income/poverty, education, employment, housing, and occupation) in relation to adverse prenatal events. Major et al. [46] applied the same index to evaluate associations with all-cause cancer, cardiovascular disease, and mortality. Derivation of an environmental quality index holds promise for improving the linkage between the impact of the overall environment and health [47]. Limitations
As a single-question qualitative measurement, SRH is unable to capture all aspects of health risk or health status. Burgard and Chen [48] suggested that the comparability of self-reported information about specific health conditions might vary across race and social groups, in part because of diagnosis bias. Additionally, measures of specific symptoms may differ if respondents interpret questions or concepts differently. In this analysis, the study population was limited to 20–50 year old nonsmokers, which limits the generalizability of our findings for children and the elderly. We selected this age range in part because some of the blood chemical concentrations were only available for 20–50 year olds. Additionally, the elderly have higher rates of morbidity and children are undergoing rapid developmental changes that may lead to more varied clinical and nutritional measures. Due to the cross-sectional design of the study, we cannot infer causality as the basis for any relationships observed between the explanatory variables and SRH. We did not conduct analyses to evaluate possible correlations between and amongst variables within each of the domains. Further, it is likely that many of the social factors that affect health have both independent and interactive effects on various measures of health. For example, low income is often associated with many other factors contributing to poor health outcomes (e.g., lower levels of education, substandard housing, risky health behaviors, food insecurity, and lack of health insurance coverage). Because this was an exploratory, hypothesis generating analysis, multiple testing correction approaches were not applied. Therefore, p-values should be interpreted with caution. In addition, multivariate regression models were not evaluated.
Conclusion SRH was used to delineate and explore relationships between multiple health risk factors that ultimately will help inform the design of subsequent studies by highlighting risk factors that relate to health status. To the best of our knowledge, no previous research has applied both binary and ordered logit models to study the
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relationships between SRH for such a wide range of health risk factors. Nonsmoking respondents representative of the United States population reported poorer SRH in associations with race/ethnicity, income and education level, and a majority of the health risk indicators studied, including serological measures of nutrition and health risk and blood biomarkers of environmental exposures. Our analyses, along with others [3, 12, 17, 49–51], lend support for the utility and continued validation of SRH as a reasonable proxy of health status for application in screening level community-based health and environmental studies, to identify vulnerable neighborhoods or counties, guide and prioritize public policy decisions in communities with suspected health disparities, and assist with exposure assessments, which often lack individualized health data.
Additional file Additional file 1: Table S1. Sociodemographic Domain; Table S2. Health Care Domain; Table S3. Health Status Domain; Table S4. Lifestyle Domain; Table S5. Clinical Indicators; Table S6. Environmental Scores/ Chemicals). This file details all associations between the domain factors and poorer SRH for both binary (poor/fair versus good/very good/ excellent) and ordinal 5-point scoring of SRH (1 = poor, 2 = fair, 3 = good, 4 = very good, and 5 = excellent). (XLSX 45 kb)
Abbreviations BMI, body mass index; Cd, cadmium; CI, 95 % confidence intervals; CRP, C-reactive protein; HDL, high-density lipoprotein; Hg, mercury; LOD, limit of detection; NHANES, National Health and Nutrition Examination Survey; ORs, odds ratios; Pb, lead; PIR, ratio of family income to poverty; RDW, red blood cell distribution width; SFH, single-family house; SRH, self-reported health; VOCs, volatile organic compounds Acknowledgements We thank the reviewers for their careful review and helpful comments, Whitney Krueger for review of the preliminary manuscript and helpful discussions. We also thank Kendrick Edwards, Liana Lucier, and Rayanne Antonelli for assistance with the preliminary analyses. This manuscript has been subjected to review by the US Environmental Protection Agency, National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does the mention of trade names or commercial products constitute endorsement or recommendation for use. Funding This study was funded by the National Health and Environmental Effects Research Laboratory (contract number EP-D-12-050) and the National Center for Computational Toxicology within the U.S. Environmental Protection Agency (EPA) Office of Research and Development. This project was supported in part by an appointment to the Research Participation Program at the Office of Research and Development, U.S. EPA, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA. Availability of data and materials All data used in this study were collected by the National Center for Health Statistics, Centers for Disease Control and Prevention. The data supporting the results reported in the article can be found at http://www.cdc.gov/Nchs/ Nhanes/Nhanes3/data_files.htm.
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Authors’ contributions JEG, TJW, RJB, AC, and EEH conceived and designed the data analysis. RJB analyzed the data. RJB and AAW prepared tables and figures. JEG, RJB, AAW, AC, and AJG wrote and revised the manuscript. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Consent for publication Not applicable. Ethics approval and consent to participate The NHANES survey protocol was approved by the National Center for Health Statistics Research Ethics Review Board. All participants provided written informed consent. Author details 1 Environmental Public Health Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Mail Drop: 58C, Research Triangle Park, NC 27711, USA. 2Oak Ridge Institute for Science and Education, Environmental Public Health Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Mail Drop: 58C, Research Triangle Park, NC 27711, USA. 3 Sociology Department, Whitman College, 345 Boyer Ave, Walla Walla, WA 99362, USA. 4Westat, 1600 Research Blvd, Rockville, MD 20850, USA. Received: 22 May 2015 Accepted: 16 July 2016
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RESEARCH ARTICLE
Open Access
Factors associated with self-reported health: implications for screening level community-based health and environmental studies Jane E. Gallagher1, Adrien A. Wilkie2, Alissa Cordner3, Edward E. Hudgens1, Andrew J. Ghio1, Rebecca J. Birch4 and Timothy J. Wade1*
Abstract Background: Advocates for environmental justice, local, state, and national public health officials, exposure scientists, need broad-based health indices to identify vulnerable communities. Longitudinal studies show that perception of current health status predicts subsequent mortality, suggesting that self-reported health (SRH) may be useful in screening-level community assessments. This paper evaluates whether SRH is an appropriate surrogate indicator of health status by evaluating relationships between SRH and sociodemographic, lifestyle, and health care factors as well as serological indicators of nutrition, health risk, and environmental exposures. Methods: Data were combined from the 2003–2006 National Health and Nutrition Examination Surveys for 1372 nonsmoking 20–50 year olds. Ordinal and binary logistic regression was used to estimate odds ratios and 95 % confidence intervals of reporting poorer health based on measures of nutrition, health condition, environmental contaminants, and sociodemographic, health care, and lifestyle factors. Results: Poorer SRH was associated with several serological measures of nutrition, health condition, and biomarkers of toluene, cadmium, lead, and mercury exposure. Race/ethnicity, income, education, access to health care, food security, exercise, poor mental and physical health, prescription drug use, and multiple health outcome measures (e.g., diabetes, thyroid problems, asthma) were also associated with poorer SRH. Conclusion: Based on the many significant associations between SRH and serological assays of health risk, sociodemographic measures, health care access and utilization, and lifestyle factors, SRH appears to be a useful health indicator with potential relevance for screening level community-based health and environmental studies. Keywords: Self-reported health, Screening level health assessment, Clinical measures, Metal mixtures analyses, NHANES
* Correspondence: [email protected] 1 Environmental Public Health Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Mail Drop: 58C, Research Triangle Park, NC 27711, USA Full list of author information is available at the end of the article © 2016 Gallagher et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Gallagher et al. BMC Public Health (2016) 16:640
Background Health outcomes are multi-determined and result from complex interactions of social, cultural, economic, psychosocial, environmental, and community factors. However, this wide range of factors are typically studied in a ‘siloed’ manner [1]. Effective public health policies can be generated only if a range of risks along the complex causal chain leading to health outcomes is assessed, defined, and studied comprehensively [2]. Self-reported health (SRH) is a qualitative singlequestion assessment of health [3]. SRH is commonly acquired in health surveys in the United States (e.g., MacArthur Field Study of Successful Aging, Hawaii Health Survey, San Luis Valley Diabetes Study, National Risk Survey, National Health and Nutrition Examination Survey [NHANES], and Robert Wood Johnson Foundation) [4–7] and internationally (e.g., Spanish National Disability Survey, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, National Population Health Survey, and Manitoba Longitudinal Study on Aging) [8–10]. SRH is also commonly used in psychological research, clinical settings, and in general population surveys [11]. Studies have shown that SRH is associated with lifestyle related diseases (e.g., diabetes and hypertension [12]), lifestyle habits (e.g., smoking status [13], regular physical exercise [14], obesity [15], and, most notably, subsequent mortality [4, 10]). The validity and value of SRH, with respect to mortality, is independent of clinical or physician assessments, and SRH surpasses these measures in predictive power [11]. Few studies link SRH with diagnostic clinical indicators of disease [12, 16, 17] and even fewer evaluate SRH in relation to blood or urinary based biomarkers of environmental exposure [17, 18]. Many diseases and health conditions are often not reported, thus county, state, and national surveys often have limited health outcome data [19]. Local, state, and national public health officials, exposure scientists, and environmental justice advocates would benefit from a screening level health status indicator, such as SRH, to identify potentially vulnerable communities and modifiable health risk factors. Such an indicator would also add value to studies where both environmental exposures and social determinants of health are simultaneously assessed [20]. This study investigates the utility of SRH as a general proxy for health status by investigating whether, and to what extent, SRH is associated with race/ethnicity and broad range of health-risk indicators (N = 57) thought to be important determinates of health. Data were extracted from NHANES and include race/ethnicity and health risk factors across six domains: sociodemographic,
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health care, health status (e.g., diseases/health conditions), lifestyle factors, serological clinical and nutritional indicators, and blood biomarkers of exposures for metals and volatile organic compounds.
Methods Physical, medical, laboratory, and respondent data from questionnaires and clinical analysis were extracted from publically available NHANES data from survey years 2003–2004 and 2005–2006. The data and more information about data collection are available online [21]. Data on SRH and a broad array of subjective and objective respondent characteristics, including sociodemographic indicators, health care, lifestyle factors, and diseases, were obtained from interviewer administrated computerassisted personal interviews conducted at the household interview and mobile examination center [22, 23]. While all NHANES participants complete a computer assisted personal interview, full serum analysis, including chemical exposure assessment, is conducted for only a randomly selected subset of NHANES participants. Study population
Of the full NHANES study sample of 5214 participants between the ages of twenty and fifty, the study population for this analysis is composed of 1372 twenty to fifty year old nonsmokers with complete data on SRH and serum biomarkers. Respondents (N = 1731) were omitted from the analysis if their serum cotinine concentration was greater than 10 ng/mL (N = 1648), or if serum cotinine was missing and they self-identified as a current smoker (N = 81), or if both were missing (N = 2). We restricted the analysis to current nonsmokers due to the adverse health impact associated with smoking. We did not want to overly influence (weaken or strengthen) any potential associations between SRH and the various factors by including smokers. We verified the suspected strong relationship between SRH and smoking in preliminary analyses (not shown) that found smokers, both self-identified current smokers and participants with cotinine measurements >10 ng/mL, were twice as likely to report poor/fair health as compared to nonsmokers. An additional 2111 respondents were excluded due to missing values for benzene and/or toluene (N = 1948) or due to missing data for SRH and pertinent demographic, body measurement, and clinical data (N = 163). If data were missing from less than 20 participants for other variables, those participants were excluded from analysis using that variable. If data were missing from more than 20 participants, a “missing” category was included for analysis of that variable. Sample sizes are provided in Tables 1 and 2. Some variables of interest were only analyzed for females (e.g., ferritin, transferrin receptor, transferrin saturation, iron, hemoglobin, and total iron
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Table 1 Sociodemographic characteristics of the study population (N = 1372 twenty to fifty year old nonsmokers) N
Weighted percenta (SE)
Male
547
46.4 (1.4)
Married
Female
825
53.6 (1.4)
Never married
315
20.1 (1.7)
Other
244
16.1 (1.3)
Sex
N
Weighted percenta (SE)
813
63.8 (2.0)
Marital Status
Race/Ethnicity Mexican American
350
10.8 (1.2)
Other hispanic
61
4.9 (0.9)
median of nonsmokers) f Benzene and toluene (1 if > median of nonsmokers)
constancy of the odds ratio across multiple categories. These two models provide complementary but different interpretations of the association between SRH and the health risk indicators. For a more rigorous comparison and discussion of binary and several ordered SRH analytic choices, see Manor et al. [29] and Barger [30]. Mexican Americans and non-Hispanic Blacks, when compared to non-Hispanic Whites, were more likely to report poorer SRH. These findings are consistent with Shetterly et al. [5] and Benjamins et al. [31]. Our analyses showed a strong association with poorer SRH with lower education and income levels. Lahelma et al. [32] explain the clear associations between health and education, occupational class, and family income. Adler and Ostrove [33] discussed how sociodemographic and environmental factors, individual psychological and behavioral factors, and biological predispositions and processes can impact health status. Associations were observed between poorer SRH and the number of days a respondent’s mental health was not good. This finding suggests that SRH incorporates a
mental health or psychosocial component that otherwise would go undetected in serological based clinical tests. Poorer SRH was associated with lower levels of Vitamin C, Vitamin D, and calcium. These findings are consistent with Radimer et al. [34], who showed that intake of multivitamin and multi-minerals dietary supplements by US adults was associated with very good/excellent self-reported health. Poorer SRH was associated with lower levels of HDL, higher levels CRP, triglycerides, serum glucose, glycohemoglobin, platelet count, elevated eosinophil, and lymphocyte number. Nine of eleven blood iron markers were associated with SRH. These health indicators are linked to cardiac health, diabetes risk, and other medical conditions. Of particular note in our study was the strong association between the RDW and poorer SRH. Several studies have reported strong associations between RDW and mortality, although the mechanism by which RDW influences health status is unknown [35–37]. Based on the strong associations observed between RDW and SRH and because RDW is
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routinely performed, RDW may serve as an important early indicator of adverse health status prior to disease onset. Biomarkers of chemical exposure
Blood levels of the three toxic heavy metals (cadmium, lead, mercury) and two VOCs (toluene and benzene) were evaluated in relation to SRH. All five chemicals have public health importance due to their environmental abundance and well-documented toxicity. SRH was associated with blood levels of cadmium, mercury, lead, and toluene but not benzene (perhaps because only 43 % of the respondents in this study were above the limit of detection for benzene). Examination of benzene in relation to health is of interest in light of studies showing ambient air levels of benzene and formaldehyde contribute nearly 60 % of the total cancer-related health impacts of air pollution in the United States [38]. People are exposed to mixtures of pollutants, through a variety of media, including air, water, and food. Thus, research is needed to better understand the cumulative risks posed to human health from the myriad of environmental contaminants that can occur simultaneously. Interactive effects of chemical within mixtures are complex and can result in alterations in the distribution, metabolism, absorption, and excretion of the chemicals [39]. Recently, Cobbina et al. [40] observed synergistic effects of metals mixtures which is consistent with our data where the odds of reporting poorer SRH were greater if the combined blood levels of mercury, lead, and cadmium were considered as opposed to each of the individual metals. In isolation, increasing levels of blood mercury were associated with a better SRH, an association that is likely confounded by income and fish consumption. For example, Mahaffey et al. [41] showed that blood mercury levels in women was related to higher income, consumption of fish, ethnicity, and residence (census region and coastal proximity). Higher blood lead and cadmium levels were associated with lower income levels [42]. Taken together, these studies underscore the need for further research into the relationships between health and cumulative exposures to chemicals, in the context of cultural, economic factors, especially for vulnerable populations and communities [43]. Our data suggest that SRH may be a useful screeninglevel indicator of health status for community-based health and environmental studies based on the number of associations of SRH of several sociodemographic, health care, health, lifestyle, serum-based nutritional, and serum-based environmental measurements. Examples of studies using screening level indices are those by Gallagher et al. [44] where health, sustainability, and environmental indices were derived for fifty major US cities. These diverse indices and associated indicators from
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which they were derived were associated with disparities related to race, education, and income. Messer et al. [45] applied a multidimensional neighborhood deprivation index (which considered income/poverty, education, employment, housing, and occupation) in relation to adverse prenatal events. Major et al. [46] applied the same index to evaluate associations with all-cause cancer, cardiovascular disease, and mortality. Derivation of an environmental quality index holds promise for improving the linkage between the impact of the overall environment and health [47]. Limitations
As a single-question qualitative measurement, SRH is unable to capture all aspects of health risk or health status. Burgard and Chen [48] suggested that the comparability of self-reported information about specific health conditions might vary across race and social groups, in part because of diagnosis bias. Additionally, measures of specific symptoms may differ if respondents interpret questions or concepts differently. In this analysis, the study population was limited to 20–50 year old nonsmokers, which limits the generalizability of our findings for children and the elderly. We selected this age range in part because some of the blood chemical concentrations were only available for 20–50 year olds. Additionally, the elderly have higher rates of morbidity and children are undergoing rapid developmental changes that may lead to more varied clinical and nutritional measures. Due to the cross-sectional design of the study, we cannot infer causality as the basis for any relationships observed between the explanatory variables and SRH. We did not conduct analyses to evaluate possible correlations between and amongst variables within each of the domains. Further, it is likely that many of the social factors that affect health have both independent and interactive effects on various measures of health. For example, low income is often associated with many other factors contributing to poor health outcomes (e.g., lower levels of education, substandard housing, risky health behaviors, food insecurity, and lack of health insurance coverage). Because this was an exploratory, hypothesis generating analysis, multiple testing correction approaches were not applied. Therefore, p-values should be interpreted with caution. In addition, multivariate regression models were not evaluated.
Conclusion SRH was used to delineate and explore relationships between multiple health risk factors that ultimately will help inform the design of subsequent studies by highlighting risk factors that relate to health status. To the best of our knowledge, no previous research has applied both binary and ordered logit models to study the
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relationships between SRH for such a wide range of health risk factors. Nonsmoking respondents representative of the United States population reported poorer SRH in associations with race/ethnicity, income and education level, and a majority of the health risk indicators studied, including serological measures of nutrition and health risk and blood biomarkers of environmental exposures. Our analyses, along with others [3, 12, 17, 49–51], lend support for the utility and continued validation of SRH as a reasonable proxy of health status for application in screening level community-based health and environmental studies, to identify vulnerable neighborhoods or counties, guide and prioritize public policy decisions in communities with suspected health disparities, and assist with exposure assessments, which often lack individualized health data.
Additional file Additional file 1: Table S1. Sociodemographic Domain; Table S2. Health Care Domain; Table S3. Health Status Domain; Table S4. Lifestyle Domain; Table S5. Clinical Indicators; Table S6. Environmental Scores/ Chemicals). This file details all associations between the domain factors and poorer SRH for both binary (poor/fair versus good/very good/ excellent) and ordinal 5-point scoring of SRH (1 = poor, 2 = fair, 3 = good, 4 = very good, and 5 = excellent). (XLSX 45 kb)
Abbreviations BMI, body mass index; Cd, cadmium; CI, 95 % confidence intervals; CRP, C-reactive protein; HDL, high-density lipoprotein; Hg, mercury; LOD, limit of detection; NHANES, National Health and Nutrition Examination Survey; ORs, odds ratios; Pb, lead; PIR, ratio of family income to poverty; RDW, red blood cell distribution width; SFH, single-family house; SRH, self-reported health; VOCs, volatile organic compounds Acknowledgements We thank the reviewers for their careful review and helpful comments, Whitney Krueger for review of the preliminary manuscript and helpful discussions. We also thank Kendrick Edwards, Liana Lucier, and Rayanne Antonelli for assistance with the preliminary analyses. This manuscript has been subjected to review by the US Environmental Protection Agency, National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does the mention of trade names or commercial products constitute endorsement or recommendation for use. Funding This study was funded by the National Health and Environmental Effects Research Laboratory (contract number EP-D-12-050) and the National Center for Computational Toxicology within the U.S. Environmental Protection Agency (EPA) Office of Research and Development. This project was supported in part by an appointment to the Research Participation Program at the Office of Research and Development, U.S. EPA, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA. Availability of data and materials All data used in this study were collected by the National Center for Health Statistics, Centers for Disease Control and Prevention. The data supporting the results reported in the article can be found at http://www.cdc.gov/Nchs/ Nhanes/Nhanes3/data_files.htm.
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Authors’ contributions JEG, TJW, RJB, AC, and EEH conceived and designed the data analysis. RJB analyzed the data. RJB and AAW prepared tables and figures. JEG, RJB, AAW, AC, and AJG wrote and revised the manuscript. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Consent for publication Not applicable. Ethics approval and consent to participate The NHANES survey protocol was approved by the National Center for Health Statistics Research Ethics Review Board. All participants provided written informed consent. Author details 1 Environmental Public Health Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Mail Drop: 58C, Research Triangle Park, NC 27711, USA. 2Oak Ridge Institute for Science and Education, Environmental Public Health Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Mail Drop: 58C, Research Triangle Park, NC 27711, USA. 3 Sociology Department, Whitman College, 345 Boyer Ave, Walla Walla, WA 99362, USA. 4Westat, 1600 Research Blvd, Rockville, MD 20850, USA. Received: 22 May 2015 Accepted: 16 July 2016
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